Data Augmentation by Pairing Samples for Images Classification (1801.02929v2)

Abstract: Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch from an original image. In this paper, we introduce a simple but surprisingly effective data augmentation technique for image classification tasks. With our technique, named SamplePairing, we synthesize a new sample from one image by overlaying another image randomly chosen from the training data (i.e., taking an average of two images for each pixel). By using two images randomly selected from the training set, we can generate $N^2$ new samples from $N$ training samples. This simple data augmentation technique significantly improved classification accuracy for all the tested datasets; for example, the top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show that our SamplePairing technique largely improved accuracy when the number of samples in the training set was very small. Therefore, our technique is more valuable for tasks with a limited amount of training data, such as medical imaging tasks.

• The paper introduces SamplePairing, a novel augmentation technique that averages image pairs to create diverse samples and significantly reduce top-1 error rates on benchmark datasets.
• The method generates up to N² synthesized samples from N originals while preserving class labels, offering a simple yet effective approach to mitigate overfitting.
• Empirical results highlight notable improvements, with error reductions from 33.5% to 29.0% on ILSVRC 2012 and from 8.22% to 6.93% on CIFAR-10, and even more pronounced gains on smaller datasets.

Overview of SamplePairing Data Augmentation in Image Classification

In the domain of machine learning, specifically in image classification tasks, data augmentation has long been an established method for mitigating overfitting and enhancing the robustness of trained models. The paper "Data Augmentation by Pairing Samples for Image Classification," authored by Hiroshi Inoue, introduces a novel yet straightforward augmentation technique known as SamplePairing, which synthesizes new training samples by averaging pairs of images from the existing dataset. This augmentation method showcases substantial improvements in classification accuracy across various datasets, including well-known benchmarks such as ILSVRC 2012 and CIFAR-10.

Methodology and Results

Inoue's technique involves creating a new sample by overlaying one image with another image, randomly selected from the training set, effectively producing $N^2$ synthesized samples from $N$ original samples. The label for the synthesized sample is inherited from one of the original images, thereby preserving class consistency. Experiments demonstrate that SamplePairing significantly enhances accuracy. For instance, it achieved a reduction in the top-1 error rate from 33.5% to 29.0% on the ILSVRC 2012 dataset utilizing GoogleNet, and from 8.22% to 6.93% on the CIFAR-10 dataset.

A noteworthy observation is the technique's increased efficacy on smaller datasets. When trained on only a fraction of the CIFAR-10 dataset, SamplePairing yielded more pronounced improvements, exemplified by a decrease in the classification error rate from 43.1% to 31.0% with just 100 samples per class. This result underscores the potential value of SamplePairing in applications with constrained data availability, such as medical imaging.

Comparative Analysis and Implications

The approach taken by SamplePairing contributes distinctly to the broader landscape of data augmentation methods. Unlike techniques such as SMOTE, which synthesizes samples by interpolating between two minority class samples in feature space, SamplePairing's utility extends across all classes, applying uniform weight when averaging image pairs. Additionally, the comparison with contemporary methods like mixup emphasizes its uniqueness. Mixup often blends both data and labels, using randomized weights to determine contribution from each sample. In contrast, SamplePairing's deterministic uniform averaging approach eschews blending labels, which makes it notably simpler to integrate into existing training pipelines without substantial modifications to the neural network architecture.

The paper indicates that the methodology robustness is not highly sensitive to intermittent disabling of the augmentation process, which empirically led to optimal validation results. Additionally, varying the mechanism of selecting the secondary image to overlay influenced the results, with unrestricted random selection from all classes yielding the greatest accuracy gains.

Theoretical and Practical Considerations

Future exploration to establish a solid theoretical framework for SamplePairing could provide insight into its mechanism of action, potentially guiding refinement of hyperparameters or suggesting analogous techniques applicable to other domains within machine learning, such as Generative Adversarial Networks.

Practically, the straightforward implementation of this method could accelerate its adoption in real-world applications where data scarcity is a critical constraint, allowing researchers and practitioners to exploit large networks in data-limited contexts more effectively.

Conclusion

Inoue's SamplePairing method serves as an effective data augmentation strategy, offering significant improvement in classification capabilities of convolutional neural networks, especially when training data is sparse. The simplicity and effectiveness of SamplePairing, validated by empirical results, position it as a valuable tool in the arsenal of techniques for combating overfitting and improving model generalization, with promising implications for future applications across different machine learning challenges.